Fast and Accurate Uncertainty Estimation for Galaxy Surveys Using RascalC
Location
Havener Center, Meramec Gasconade Room, 9:30am-11:30am
Start Date
4-2-2026 10:00 AM
End Date
4-2-2026 10:30 AM
Presentation Date
April 2, 2026; 10:00am-10:30am
Description
Understanding how galaxies are distributed across the universe is a powerful tool for uncovering the fundamental laws of physics. A key challenge in this work is accurately estimating the uncertainties in our measurements, a task that traditionally requires running thousands of computationally expensive simulations. In this project, we implement and validate RascalC, a fast semi-analytical method that computes these uncertainties in under 100 CPU-hours roughly 10,000 times faster than simulation-based approaches. We apply this pipeline to the HETDEX survey, which maps hundreds of thousands of distant galaxies. Using synthetic galaxy catalogs, we demonstrate that RascalC reproduces the uncertainty structure of full simulations with excellent accuracy while producing significantly smoother results, establishing a robust and efficient pipeline for future large-scale galaxy surveys.
References:
Philcox, Oliver HE, et al. "RASCALC: a jackknife approach to estimating single-and multitracer galaxy covariance matrices." Monthly Notices of the Royal Astronomical Society 491.3 (2020): 3290-3317.
Pérez-Fernández, A., et al. "Semi-analytical covariance matrices for two-point correlation function for DESI 2024 data." arXiv preprint arXiv:2404.03007 (2024).
Agrawal, Aniket, et al. "Generating log-normal mock catalog of galaxies in redshift space." Journal of Cosmology and Astroparticle Physics 2017.10 (2017): 003-003.
Li, Yin, et al. "Disconnected covariance of 2-point functions in large-scale structure." Journal of Cosmology and Astroparticle Physics 2019.01 (2019): 016-016.
Gebhardt, Karl, et al. "The Hobby–Eberly telescope dark energy experiment (HETDEX) survey design, reductions, and detections." The Astrophysical Journal 923.2 (2021): 217.
Biography
Deeshani Mitra is a second-year PhD student in Physics at the Missouri University of Science and Technology, working under the supervision of Dr. Shun Saito. Her research focuses on computational cosmology, large-scale structure, and the statistical analysis of cosmological survey data. She completed her B.Sc. in Physics from the University of Calcutta, India, where she developed a strong foundation in theoretical and experimental physics. She then pursued an M.Sc. in Physics with a specialization in Astrophysics at St. Xavier’s College, Kolkata, graduating among the top students of her class and receiving a gold medal for academic excellence.
Meeting Name
2026 - Miners Solving for Tomorrow Research Conference
Department(s)
Physics
Document Type
Presentation
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2026 The Authors, All rights reserved
Fast and Accurate Uncertainty Estimation for Galaxy Surveys Using RascalC
Havener Center, Meramec Gasconade Room, 9:30am-11:30am
Understanding how galaxies are distributed across the universe is a powerful tool for uncovering the fundamental laws of physics. A key challenge in this work is accurately estimating the uncertainties in our measurements, a task that traditionally requires running thousands of computationally expensive simulations. In this project, we implement and validate RascalC, a fast semi-analytical method that computes these uncertainties in under 100 CPU-hours roughly 10,000 times faster than simulation-based approaches. We apply this pipeline to the HETDEX survey, which maps hundreds of thousands of distant galaxies. Using synthetic galaxy catalogs, we demonstrate that RascalC reproduces the uncertainty structure of full simulations with excellent accuracy while producing significantly smoother results, establishing a robust and efficient pipeline for future large-scale galaxy surveys.
References:
Philcox, Oliver HE, et al. "RASCALC: a jackknife approach to estimating single-and multitracer galaxy covariance matrices." Monthly Notices of the Royal Astronomical Society 491.3 (2020): 3290-3317.
Pérez-Fernández, A., et al. "Semi-analytical covariance matrices for two-point correlation function for DESI 2024 data." arXiv preprint arXiv:2404.03007 (2024).
Agrawal, Aniket, et al. "Generating log-normal mock catalog of galaxies in redshift space." Journal of Cosmology and Astroparticle Physics 2017.10 (2017): 003-003.
Li, Yin, et al. "Disconnected covariance of 2-point functions in large-scale structure." Journal of Cosmology and Astroparticle Physics 2019.01 (2019): 016-016.
Gebhardt, Karl, et al. "The Hobby–Eberly telescope dark energy experiment (HETDEX) survey design, reductions, and detections." The Astrophysical Journal 923.2 (2021): 217.
Comments
Advisor: Shun Saito, saitos@mst.edu